Urban Security Project

Objective

The objective of this project is to develop a next-generation active solution for simulating, predicting, and visualizing the propagation of airborne contaminants in complex urban environments with embedded sensors using graphics hardware acceleration and sophisticated numerical methods to model multi-component flow dynamics in real-time.

This project is part of several New York City and Long Island dispersion programs in collaboration with the Environment Measurement Laboratory (EML) in New York City (NYC), Brookhaven National Laboratory (BNL), Northrop Grumman Co., and other companies, labs and government agencies. The broad goal of of these programs is to provide a new modeling system for atmospheric dispersion of airborne hazardous materials (nuclear, chemical, biological) in dense urban environments as a key tool for use in emergency management. The specific objectives are to provide new modeling capabilities for deep urban canyons and indoors; evaluate, improve and validate both wind and dispersion models; train end-users in the fundamentals of modeling concepts and dispersion of plumes; and plan future R&D efforts needed to provide emergency managers and first-responders the best available information regarding the spatial extent and timing of hazardous conditions to minimize the consequences of terrorist attacks using hazardous agents.

Description

We employ a relatively new computational fluid dynamics model, the Lattice Boltzmann Method (LBM). Unlike other approaches, LBM discretizes the micro-physics of local interactions and can handle very complex boundary conditions, such as deep urban canyons, curved walls, indoors, and dynamic boundaries of moving objects. Furthermore, its computational pattern which is similar to Cellular Automata is easily parallelizable and hence can be accelerated on commodity graphics processing units (GPUs), achieving real-time or even accelerated real-time on ordinary PCs and laptops, providing a predictive tool for anticipating subsequent propagation. Another key innovation of LBM is its extension to support input from pervasive sensors. This will allow us to influence the simulation so as to maintain its faithfulness to real-time sensor readings.

We have implemented the parallel LBM computation on a cluster of GPUs. Our cluster, called the Stony Brook Visual Computing Cluster, has 32 nodes connected by a 1 Gigabit network switch. Each node is an HP PC equipped with a GPU, Nvidia GeForce FX 5800 Ultra. We have tested the LBM simulation with a GIS of Times Square Area of NYC, which consists of 91 blocks and roughly 850 buildings. We have also test it with a 10 blocks GIS around the EML building in the West Village of NYC, overlaid with results of dispersion simulation and real-time readings from 3 sensors installed on that building. Besides, we have implemented a 3D city navigation system (web-based or stand alone), featuring a 3D polygonal model GIS with façade texturing, flow visualization streamlines, volume rendering plumes, and information visualization of real-time sensor data.